The generator consists of three networks, namely, \(G^s, G^m, G^l\) , each of which corresponds
to different scales.
We utilize the smallest scale generator as the base generator for the entire training process.
The base generator is built with a skip connection [1]} to maintain the relation of
low level and high level features.
The training process starts from images with 128 \(\times \)  256 resolution as the smallest resolution.
To train the next hierarchy, we unify the base generator \(G^s\)  with generator \(G^m\) .
In the largest scale, generator \(G^l\)  is unified with \(G^m\)  and \(G^s\)  to form a single generator with increased capacity.
The weight parameters trained from the small-scale network are reused in the large-scale one.
During the training process in the medium-scale network, the trained weight is reused and fine-tuned to produce medium resolution images.
The training process for the next scale follows the same rule until the original scale is reached.
